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How to Value Imperfect Information 

Presenter: Ron Allred and Jon Anker, ConocoPhillips

Presented at the 2003 DAAG Conference in Houston, Texas.

Difficulties associated with decision-making under uncertainty are familiar to most people.  Companies and individuals regularly make decisions where important information about factors that could significantly affect the outcome of decisions is lacking.  Two general patterns with regards to decision-making can be recognized.

-       A general EMV pattern, where the decisions occur up front and then all the uncertainties occur after those decisions are made. 

-       A phased decision pattern, where the decisions are interspersed with the uncertainties.

A phased decision pattern is indicative of a value of information situation.

A value of information situations requires the assessment of whether the acquisition of information would affect future decisions.  For most circumstances the information to be collected is not perfect, the prediction may be wrong.  Hopefully once information has been acquired a better perception about the likelihood’s of the states of nature will exist, but because the information is imperfect we will not have complete information about the actual state of nature.

When evaluating the viability of acquiring information, the most straightforward approach is to use decision tree analysis to determine whether the EMV of the project is greater than or equal to the project without acquired information.  The normal steps to take in this type of analysis would be to;

1.      Establish actual(prior) probabilities.  These are the actual probabilities for some event prior to collecting information.

2.      Establish indicated(conditional) probabilities.  These are the probabilities indicated by the acquired information if an actual event actually happened.

3.      Determine the posteriorprobabilities using Bayes’ Theorem.  Determining the probabilities of the outcome of an actual (with prior probability) event following the acquisition of information (indicated probabilities).

4.      Comparison of EMV’s with and without the acquired information.

Conceptually we can solve most EMV problems using decision trees, however if more and more uncertainty events are added to a decision tree model then the sudden increase in the number of branches makes calculations both cumbersome and impractical.  It has now become routine for EMV calculations to be done using electronic spreadsheet programs based on "Monte Carlo” randomised simulation.  These programs use a random sampling process to generate inputs from uncertainty distributions, which are then processed by a mathematical model.

While Monte Carlo simulation programs are easy to use and can deal with a large number of uncertainty events, they are not well suited for use in value of information situations.  There is no difficulty in constructing probability distributions for actual (prior) and indicated (conditional) events, but it is very difficult to construct a resultant posteriorprobability distribution.

Based on simulation work concerning the value of appraisal drilling information prior to field development, we have devised a methodology that allows for determining the value of information using Monte Carlo sampling techniques.  This methodology is based on comparisons of indicated (conditional) probabilities with compressed ranges associated with actual (prior) probability determinations.

Keywords: Value of information voivoc valoi, modeling modtree, decision trees dectree, simulation mcsim

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